Computers are increasingly used to plan complex surgeries by analyzing preoperative Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans of a patient. In order to execute the surgical plan, it is important to accurately align or register the three dimensional preoperative data to an actual location of the anatomical features of interest during surgery.
One conventional technique for performing this type of registration is to attach a stereo-tactic frame or fiducial markers to the patient, and to precisely locate the frame or markers both prior to and during surgery.
For example, a conventional registration protocol includes implanting three metallic markers or pins in a patient's femur, one proximally in the trochanter and two distally in the condyles, near the knee. The insertion of the pins requires minor surgery. A CT-scan image of the patient is subsequently acquired. By analyzing the CT data, the surgeon decides upon the size and location of the implant that best fits the patient's anatomy. During surgery, the metallic pins are exposed at the hip and knee. The patient's leg is attached to a surgical robot device that must then locate the exposed pins. A registration, or coordinate transformation from CT space to robot space, is computed using the locations of the three pins as a Cartesian frame. The accuracy of this registration has been measured to be better than one millimeter. However, the use of such pins as markers is not always desirable, as they may cause significant patient discomfort, and the required surgical procedure to insert and subsequently remove the pins results in inconvenience and additional cost to the patient.
An alternative technique is to perform anatomy-based registration that uses anatomical features of the patient, generally bony features, as markers for registration.
Conventional methods for anatomy-based registration of three dimensional volume data to projection data include three techniques, described by Lavallee et al. in "Matching 3-D smooth surfaces with their 2-D projections using 3-D distance maps", proceedings of Geometric Methods in Computer Vision, SPIE vol. 1570, pages 322-336, 1991; by Lee in a PhD Thesis on "Stereo Matching of Skull Landmarks", from Stanford University in 1991; and by Feldmar et al. in Technical Report No. 2434, "3D-2D projective registration of free-form curves and surfaces" from INRIA, Sophia Antipolis, 1994.
In the approach of Lavallee et al., "Matching 3-D smooth surfaces with their 2-D projections using 3-D distance maps", calibrated video images are used to register a model of a vertebra to its projections. A hierarchical volume is built that is used to query the closest point from anatomical surfaces to projection lines. Also defined is a negative distance to address the situation of lines intersecting the surface.
In the approach described by Lee, "Stereo Matching of Skull Landmarks", stereo pairs of radiographs are used to track in real time the position of a patient's skull during radiotherapy delivery. Localized bony features that are segmented from a CT-scan are employed for this purpose.
In the approach described by Feldmar et al., "3D-2D projective registration of free-form curves and surfaces", surfaces are registered to projected contours. This is accomplished by defining image-to-surface correspondences and by minimizing a least squares criterion using iterative methods. The criterion incorporates contour and surface normals. This method accommodates errors in calibration by allowing optimization of the camera parameters.
Conventional methods for performing geometric calibration of images include a two-plane method described by Martin in "Camera Models Based on Data from Two Calibration Planes", published in Computer Graphics and Image Processing, 1981, volume 17, pages 173-180, and a method described by Champleboux et al. in "Accurate Calibration of Cameras and Range Imaging Sensors: The NPBS Method" published in ICRA Conference Proceedings, 1992, pages 1552-1557.
The calibration of distortion-free radiographs was investigated by Brown, "Registration of planar film radiographs with computed tomography", in Mathematical Methods in Biomedical Image Analysis, pages 42-51, San Francisco, Calif., June 1996, IEEE.
Most of the above described techniques, with the notable exception of Lee's, uses high quality three dimensional and projection images for experiments, such as high resolution CT scans of dry bones, or simulations of radiographs using video images. However, such high quality data is typically only available in a controlled laboratory test, and is superior to the data that would normally be clinically available. For example, typical CT slices also show soft tissue, present notable artifacts, and are of wide and unequal spacing to minimize the x-ray doses delivered to the patient. A precise segmentation of such data presents a very challenging problem. Furthermore, most fluoroscopic images that are obtained with commonly available clinical devices are characterized by a narrow field of view (FOV), typically with a maximum FOV of 100 mm, and include significant noise and distortion.
As such, there exists a need to provide an improved system and method for accomplishing an anatomy-based registration of three-dimensional data (model data) obtained from a scan, such as a CT scan or an MRI scan, to two dimensional projection data, such as x-ray data, enabling the registration of a surgical robot to a preoperative treatment plan.